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39
On the capacity of fading MIMO broadcast channels with imperfect transmitter sideinformation
 in Annual Allerton Conference on Communication, Control, and Computing
, 2005
"... A fading broadcast channel is considered where the transmitter employs two antennas and each of the two receivers employs a single receive antenna. It is demonstrated that even if the realization of the fading is precisely known to the receivers, the high signaltonoise (SNR) throughput is greatly ..."
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Cited by 29 (2 self)
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A fading broadcast channel is considered where the transmitter employs two antennas and each of the two receivers employs a single receive antenna. It is demonstrated that even if the realization of the fading is precisely known to the receivers, the high signaltonoise (SNR) throughput is greatly reduced if, rather than knowing the fading realization precisely, the trasmitter only knows the fading realization approximately. The results are general and are not limited to memoryless Gaussian fading. 1
Low complexity user selection algorithms for multiuser MIMO systems with block diagonalization
 IEEE Trans. Sig. Proc
, 2006
"... Abstract — Block diagonalization (BD) is a precoding technique that eliminates interuser interference in downlink multiuser multipleinput multipleoutput (MIMO) systems. With the assumptions that all users have the same number of receive antennas and utilize all receive antennas when scheduled for ..."
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Cited by 24 (9 self)
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Abstract — Block diagonalization (BD) is a precoding technique that eliminates interuser interference in downlink multiuser multipleinput multipleoutput (MIMO) systems. With the assumptions that all users have the same number of receive antennas and utilize all receive antennas when scheduled for transmission, the number of simultaneously supportable users with BD is limited by the ratio of the number of basestation transmit antennas to the number of user receive antennas. In a downlink MIMO system with a large number of users, the basestation may select a subset of users to serve in order to maximize the total throughput. The bruteforce search for the optimal user set, however, is computationally prohibitive. We propose two lowcomplexity suboptimal user selection algorithms for multiuser MIMO systems with BD. Both algorithms aim to select a subset of users such that the total throughput is nearly maximized. The first user selection algorithm greedily maximizes the total throughput, whereas the criterion of the second algorithm is based on the channel energy. We show that both algorithms have linear complexity in the total number of users and achieve around 95 % of the total throughput of the complete search method in simulations. I.
High SNR Analysis for MIMO Broadcast Channels: Dirty Paper Coding versus Linear Precoding
 IEEE TRANS. INFORM. THEORY
, 2007
"... In this correspondence, we compare the achievable throughput for the optimal strategy of dirty paper coding (DPC) to that achieved with suboptimal and lower complexity linear precoding techniques (zeroforcing and block diagonalization). Both strategies utilize all available spatial dimensions and ..."
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Cited by 14 (4 self)
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In this correspondence, we compare the achievable throughput for the optimal strategy of dirty paper coding (DPC) to that achieved with suboptimal and lower complexity linear precoding techniques (zeroforcing and block diagonalization). Both strategies utilize all available spatial dimensions and therefore have the same multiplexing gain, but an absolute difference in terms of throughput does exist. The sum rate difference between the two strategies is analytically computed at asymptotically high SNR. Furthermore, the difference is not affected by asymmetric channel behavior when each user has a different average SNR. Weighted sum rate maximization is also considered. In the process, it is shown that allocating user powers in direct proportion to user weights asymptotically maximizes weighted sum rate.
Opportunistic space division multiple access with beam selection
 IEEE TRANS. ON COMMUNICATIONS
, 2006
"... In this paper, a novel transmission technique for the multipleinput multipleoutput (MIMO) broadcast channel is proposed that allows simultaneous transmission to multiple users with limited feedback from each user. During a training phase, the base station modulates a training sequence on multiple ..."
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Cited by 14 (10 self)
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In this paper, a novel transmission technique for the multipleinput multipleoutput (MIMO) broadcast channel is proposed that allows simultaneous transmission to multiple users with limited feedback from each user. During a training phase, the base station modulates a training sequence on multiple sets of randomly chosen orthogonal beamforming vectors. Each user sends the index of the best beamforming vector and the corresponding signaltointerfenceplusnoise ratio for that set of orthogonal vectors back to the base station. The base station opportunistically determines the users and corresponding orthogonal vectors that maximize the sum capacity. Based on the capacity expressions, the optimal amount of training to maximize the sum capacity is derived as a function of the system parameters. The main advantage of the proposed system is that it provides throughput gains for the MIMO broadcast channel with a small feedback overhead, and provides these gains even with a small number of active users. Numerical simulations show that a 20 % gain in sum capacity is achieved (for a small number of users) over conventional opportunistic space division multiple access, and a 100 % gain (for a large number of users) over conventional opportunistic beamforming when the number of transmit antennas is four.
Correlation and capacity of measured multiuser MIMO channels
 in Proc. IEEE Intl. Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC
, 2008
"... Abstract—In multiuser multipleinput multipleoutput (MUMIMO) systems, spatial multiplexing can be employed to increase the throughput without the need for multiple antennas and expensive signal processing at the user equipments. In theory, MUMIMO is also more immune to most of propagation limita ..."
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Cited by 7 (6 self)
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Abstract—In multiuser multipleinput multipleoutput (MUMIMO) systems, spatial multiplexing can be employed to increase the throughput without the need for multiple antennas and expensive signal processing at the user equipments. In theory, MUMIMO is also more immune to most of propagation limitations plaguing singleuser MIMO (SUMIMO) systems, such as channel rank loss or antenna correlation. However, in this paper we show that this is not always true. We compare the capacity and the correlation of measured MUMIMO channels for both outdoor and indoor scenarios. The measurement data has been acquired using Eurecom’s MIMO Openair Sounder (EMOS). The EMOS can perform realtime MIMO channel measurements synchronously over multiple users. The results show that in most scenarios MUMIMO provides a higher throughput than SUMIMO also in the measured channels. However, in outdoor scenarios with a line of sight, the capacity drops significantly when the users are close together, due to high correlation at the transmitter side of the channel. In such a case, the performance of SUMIMO and MUMIMO is comparable. I.
Capacity of linear multiuser MIMO precoding schemes with measured channel
 data,"9th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2008
"... In multiuser multipleinput multipleoutput (MUMIMO) systems, spatial multiplexing can be employed to increase the throughput without the need for multiple antennas and expensive signal processing at the user equipments. In theory, MUMIMO is also more immune to most of propagation limitations pla ..."
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Cited by 7 (5 self)
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In multiuser multipleinput multipleoutput (MUMIMO) systems, spatial multiplexing can be employed to increase the throughput without the need for multiple antennas and expensive signal processing at the user equipments. In theory, MUMIMO is also more immune to most of propagation limitations plaguing singleuser MIMO (SUMIMO) systems, such as channel rank loss or antenna correlation. In this paper we compare the performance of different linear MUMIMO precoding schemes using real channel measurement data. The measurement data has been acquired using Eurecom’s MIMO Openair Sounder (EMOS). The EMOS can perform realtime MIMO channel measurements synchronously over multiple users. The results show that MUMIMO provides a higher throughput than SUMIMO also in the measured channels. However, the throughput in the measured channels is by far worse than the one in channels without spatial correlation. Of all the evaluated linear precoding schemes, the MMSE precoder performs best in the measured channels. 1.
On the Tradeoff Between Feedback and Capacity in Measured MUMIMO Channels
, 2009
"... In this work we study the capacity of multiuser multipleinput multipleoutput (MUMIMO) downlink channels with codebookbased limited feedback using real measurement data. Several aspects of MUMIMO channels are evaluated. Firstly, we compare the sum rate of different MUMIMO precoding schemes in ..."
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Cited by 6 (3 self)
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In this work we study the capacity of multiuser multipleinput multipleoutput (MUMIMO) downlink channels with codebookbased limited feedback using real measurement data. Several aspects of MUMIMO channels are evaluated. Firstly, we compare the sum rate of different MUMIMO precoding schemes in various channel conditions. Secondly, we study the effect of different codebooks on the performance of limited feedback MUMIMO. Thirdly, we relate the required feedback rate with the achievable rate on the downlink channel. Real multiuser channel measurement data acquired with the Eurecom MIMO OpenAir Sounder (EMOS) is used. To the best of our knowledge, these are the first measurement results giving evidence of how MUMIMO precoding schemes depend on the precoding scheme, channel characteristics, user separation, and codebook. For example, we show that having a large user separation as well as codebooks adapted to the second order statistics of the channel gives a sum rate close to the theoretical limit. A small user separation due to bad scheduling or a poorly adapted codebook on the other hand can impair the gain brought by MUMIMO. The tools and the analysis presented in this paper allow the system designer to tradeoff downlink rate with feedback rate by carefully choosing the codebook.
Dirty Paper Coding vs. Linear Precoding for MIMO Broadcast Channels
, 2006
"... We study the MIMO broadcast channel and compare the achievable throughput for the optimal strategy of dirty paper coding to that achieved with suboptimal and lower complexity linear precoding (e.g., zeroforcing and block diagonalization) transmission. Both strategies utilize all available spatial ..."
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Cited by 6 (0 self)
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We study the MIMO broadcast channel and compare the achievable throughput for the optimal strategy of dirty paper coding to that achieved with suboptimal and lower complexity linear precoding (e.g., zeroforcing and block diagonalization) transmission. Both strategies utilize all available spatial dimensions and therefore have the same multiplexing gain, but an absolute difference in terms of throughput does exist. The sum rate difference between the two strategies is analytically computed at asymptotically high SNR, and it is seen that this asymptotic statistic provides an accurate characterization at even moderate SNR levels. Weighted sum rate maximization is also considered, and a similar quantification of the throughput difference between the two strategies is computed. In the process, it is shown that allocating user powers in direct proportion to user weights asymptotically maximizes weighted sum rate.
On the Capacity and Design of Limited Feedback Multiuser MIMO Uplinks ∗
, 2008
"... The theory of multipleinput multipleoutput (MIMO) technology has been welldeveloped to increase fading channel capacity over singleinput singleoutput (SISO) systems. This capacity gain can often be leveraged by utilizing channel state information at the transmitter and the receiver. Users make ..."
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Cited by 2 (1 self)
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The theory of multipleinput multipleoutput (MIMO) technology has been welldeveloped to increase fading channel capacity over singleinput singleoutput (SISO) systems. This capacity gain can often be leveraged by utilizing channel state information at the transmitter and the receiver. Users make use of this channel state information for transmit signal adaptation. In this correspondence, we derive the capacity region for the MIMO multiple access channel (MIMO MAC) when partial channel state information is available at the transmitters, where we assume a synchronous MIMO multiuser uplink. The partial channel state information feedback has a cardinality constraint and is fed back from the basestation to the users using a limited rate feedback channel. Using this feedback information, we propose a finite codebook design method to maximize sumrate. In this correspondence, the codebook is a set of transmit signal covariance matrices. We also derive the capacity region and codebook design methods in the case that the covariance matrix is rankone (i.e., beamforming). This is motivated by the fact that beamforming is optimal in certain conditions. The simulation results show that when the number of feedback bits increases, the capacity also increases. Even with a small number of feedback bits, the performance of the proposed system is close to an optimal solution with the full feedback.